Abstract
Microgrids are widely spreading in electricity markets worldwide. Besides the security and reliability concerns for these microgrids, their operators need to address consumers’ pricing. Considering the growth of smart grids and smart meter facilities, it is expected that microgrids will have some level of flexibility to determine real-time pricing for at least some consumers. As such, the key challenge is finding an optimal pricing model for consumers. This paper, accordingly, proposes a new pricing scheme in which microgrids are able to deploy clustering techniques in order to understand their consumers’ load profiles and then assign real-time prices based on their load profile patterns. An improved weighted fuzzy average k-means is proposed to cluster load curve of consumers in an optimal number of clusters, through which the load profile of each cluster is determined. Having obtained the load profile of each cluster, real-time prices are given to each cluster, which is the best price given to all consumers in that cluster.
Highlights
IntroductionMicrogrids are expected to play a key role in future electricity markets, either as islanded or connected to the main grid
This paper aims at determining optimal pricing schemes for microgrid considering load profile of consumers
This paper proposes a clustering-based pricing scheme for microgrids through which a microgrid operator can assign proper price tariffs on its consumers based on the load curves clustered in distinctive classes
Summary
Microgrids are expected to play a key role in future electricity markets, either as islanded or connected to the main grid. Microgrid operators are responsible for delivering energy to consumers while meeting technical and economic criteria. One key issue for microgrid operator is determining proper pricing tariffs for consumers. One way is deploying the traditional flat prices for all consumers, which is deemed to be an inefficient pricing in electricity markets. An alternative is to define various price tariffs like time of use or even real-time prices. To this end microgrid operators need to understand their consumers’ characteristics so as they can assign optimal pricing schemes to various consumers
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